Solving optimization problems via neural networks has proven to be a promising approach in yielding better solutions. However, the full potential of parameterized models has yet to be fully explored. Motivated by the success of variational quantum optimization with multibasis encodings, we propose a quantum-inspired machine learning algorithm that integrates both approaches to reduce the system size as well as parameters in a neural network ansatz. We demonstrate the performance of the proposed algorithm by solving Ising chain systems, resulting in faster convergence towards the ground state energy. This study holds the potential for widespread applications across various fields that require efficient optimization for large-scale problems.